knitr::opts_chunk$set(
warning = TRUE, # show warnings during codebook generation
message = TRUE, # show messages during codebook generation
error = TRUE, # do not interrupt codebook generation in case of errors,
# usually better for debugging
inclue = TRUE,
echo = TRUE # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())
library(codebook)
library(rio)
library(labelled)
##
## Attaching package: 'labelled'
## The following object is masked from 'package:codebook':
##
## to_factor
library(flextable)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(stringr)
lab_info <- dir(path = "..", full.names = TRUE, recursive = TRUE,
include.dirs = TRUE, pattern = "Lab_Info.csv") %>%
import()
# Load raw SPV data
# Build data frame of valid SP verification responses
all_files <- list.files(path = "../data",
pattern = ".csv",
full.names = TRUE,
recursive = TRUE)
all_files <- all_files[grepl("PP|SP|jatos_results|pp|sp|Pp|Sp", all_files)]
all_list <- list()
for (i in 1:length(all_files)){
all_list[[i]] <- import(all_files[i])
all_list[[i]]$unique_id <- all_files[i]
all_list[[i]] <- all_list[[i]] %>%
mutate(across(everything(), as.character))
}
all_data <- bind_rows(all_list) %>%
select(subject_nr,
LAB_SEED,
datetime,
logfile,
task_order,
List,
PList,
Match,
Orientation,
Probe,
Target,
response_time,
correct,
PPList,
Orientation1,
Orientation2,
Identical,
Picture1,
Picture2,
opensesame_codename,
opensesame_version,
unique_id) %>%
filter(!is.na(opensesame_codename))
all_data$PSA_ID <- substr(all_data$unique_id, 9, 16)
all_data$PSA_ID <- gsub("\\/", "", all_data$PSA_ID)
all_data$subject_nr[na.omit(all_data$opensesame_codename != "osweb")] <-
gsub("../data/|SP|PP|_|-| |.csv", "", all_data$unique_id[na.omit(all_data$opensesame_codename != "osweb")])
SP_V <- all_data %>% select(
PSA_ID, subject_nr, LAB_SEED, datetime, logfile, task_order, List,
Match, Orientation, PList, Probe, Target, response_time, correct,
opensesame_codename, opensesame_version) %>%
left_join(lab_info %>% select(PSA_ID, Language), by = "PSA_ID") %>%
readr::type_convert() %>%
distinct() %>%
mutate(Language = ifelse(Language == "Magyar", "Hungarian", Language)) %>% ## Switch "Magyar" to "Hungarian"
mutate(Language = ifelse(Language == "Simple Chinese", "Simplified Chinese", Language)) %>% ## Switch "Simple Chinese" to "Simplified Chinese"
mutate(PSA_ID = str_replace(PSA_ID, "SRB_002B", "SRB_002")) %>% ## Combine two Serbian language groups based on the collectors' recommendation
mutate(Source = if_else(opensesame_codename == "osweb","osweb","site"),
Subject = paste0(Source,"_",PSA_ID,"_",subject_nr, "_", datetime)) %>% ## Compose the unique participant id
subset(Match != "F") ## Exclude fillers in SP_V
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## PSA_ID = col_character(),
## subject_nr = col_character(),
## LAB_SEED = col_character(),
## datetime = col_character(),
## logfile = col_character(),
## task_order = col_character(),
## List = col_character(),
## Match = col_character(),
## Orientation = col_character(),
## PList = col_character(),
## Probe = col_character(),
## Target = col_character(),
## response_time = col_double(),
## correct = col_double(),
## opensesame_codename = col_character(),
## opensesame_version = col_character(),
## Language = col_character()
## )
# Build data frame of valid PP responses
PP <- all_data %>% select(
PSA_ID, LAB_SEED, datetime, logfile, subject_nr, PPList,
Orientation1, Orientation2, Identical, Picture1, Picture2,
response_time, correct, opensesame_codename, opensesame_version) %>%
left_join(lab_info %>% select(PSA_ID, Language), by = "PSA_ID") %>%
readr::type_convert() %>%
distinct() %>%
mutate(Language = ifelse(Language == "Magyar", "Hungarian", Language)) %>% ## Switch "Magyar" to "Hungarian"
mutate(Language = ifelse(Language == "Simple Chinese", "Simplified Chinese", Language)) %>% ## Switch "Simple Chinese" to "Simplified Chinese"
mutate(PSA_ID = str_replace(PSA_ID, "SRB_002B", "SRB_002")) %>% ## Combine two Serbian language groups based on the collectors' recommendation
mutate(Source = if_else(opensesame_codename == "osweb","osweb","site"),
Subject = paste0(Source,"_",PSA_ID,"_",subject_nr, "_", datetime)) %>% ## Compose the unique participant id
filter(Identical != "F") # practice trials
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## PSA_ID = col_character(),
## LAB_SEED = col_character(),
## datetime = col_character(),
## logfile = col_character(),
## subject_nr = col_character(),
## PPList = col_character(),
## Orientation1 = col_character(),
## Orientation2 = col_character(),
## Identical = col_character(),
## Picture1 = col_character(),
## Picture2 = col_character(),
## response_time = col_double(),
## correct = col_double(),
## opensesame_codename = col_character(),
## opensesame_version = col_character(),
## Language = col_character()
## )
SP_V$Subject[SP_V$opensesame_codename != "osweb"] <-
paste0(SP_V$Source[SP_V$opensesame_codename != "osweb"],
"_",
SP_V$PSA_ID[SP_V$opensesame_codename != "osweb"],
"_",
SP_V$subject_nr[SP_V$opensesame_codename != "osweb"])
PP$Subject[PP$opensesame_codename != "osweb"] <- paste0(PP$Source[PP$opensesame_codename != "osweb"],"_",PP$PSA_ID[PP$opensesame_codename != "osweb"],"_",PP$subject_nr[PP$opensesame_codename != "osweb"])
PP$Subject[PP$Subject == "site_MYS_003_MYS003/MYS00324"] <- rep(c("site_MYS_003_MYS003/MYS00324", "site_MYS_003_MYS003/MYS00324_1"), each = 24)
SP_V$Subject[SP_V$Subject == "site_USA_173_USA173/USA17328
"] <- c(rep("site_USA_173_USA173_28", 30), rep("site_USA_173_USA173/USA17328_1", 29))
# fix issues with duplicate trials
SP_V <- SP_V %>%
filter(!duplicated(SP_V %>% select(Subject, Target)))
PP <- PP %>%
filter(!duplicated(PP %>% select(Subject, Picture1)))
# test subjects counts
SP_V_subjects <- SP_V %>%
group_by(Subject, PSA_ID) %>%
count()
PP_subjects <- PP %>%
group_by(Subject, PSA_ID) %>%
count()
merge_subjects <- SP_V_subjects %>%
full_join(PP_subjects, by = "Subject") %>%
rename(SP_Data = n.x,
PP_Data = n.y)
merge_subjects$PSA_ID.x[is.na(merge_subjects$PSA_ID.x)] <- merge_subjects$PSA_ID.y[is.na(merge_subjects$PSA_ID.x)]
merge_subjects$PSA_ID <- merge_subjects$PSA_ID.x
merge_subjects <- merge_subjects %>% select(-PSA_ID.x, -PSA_ID.y)
osweb_meta <- dir(path = ".."
,full.names = TRUE, recursive = TRUE,
include.dirs = TRUE, pattern = "jatos_meta.csv") %>%
import() %>%
unique() %>%
mutate(Batch = str_replace(Batch, "SRB_002B", "SRB_002")) %>%
mutate(gender = ifelse(gender==1,"FEMALE",ifelse(gender==2,"MALE","MISSING"))) %>%
# mutate(birth_year_tr = as.numeric(birth_year)) %>%
mutate(birth_year_tr = as.numeric(gsub(birth_year,pattern="NA|x",replacement = ""))) %>%
mutate(year = ifelse(birth_year_tr > 21 & !is.na(birth_year_tr), 1900 + birth_year_tr, 2000 + birth_year_tr)) %>%
mutate(age = ifelse(!is.na(year),2021-year,NA)) %>%
group_by(Batch) %>%
summarise(N = n(),
Female_N = sum(gender=="FEMALE",na.rm = TRUE),
Male_N = sum(gender=="MALE", na.rm = T),
Age = mean(age, na.rm=TRUE),
AgeSD = sd(age, na.rm=TRUE),
Proficiency = mean(lang_prof),
missing_age = sum(is.na(age)))
site_meta <- import("includes/files/insite_meta.csv") %>%
mutate(gender = ifelse(gender=="female","FEMALE",ifelse(gender=="male","MALE","MISSING"))) %>%
unique() %>%
group_by(PSA_ID) %>%
summarize(N = n(), Female_N = sum(gender=="FEMALE", na.rm = TRUE),
Male_N = sum(gender=="MALE", na.rm = T),
Age = mean(age, na.rm=TRUE),
AgeSD = sd(age, na.rm=TRUE),
missing_age = sum(is.na(age))
)
merge_subjects <- merge_subjects %>%
group_by(PSA_ID) %>%
summarize(SP_N_trials = sum(SP_Data, na.rm = T),
PP_N_trials = sum(PP_Data, na.rm = T),
SP_N = length(na.omit(SP_Data)),
PP_N = length(na.omit(PP_Data))) %>%
full_join(site_meta, by = "PSA_ID") %>%
full_join(osweb_meta, by = c("PSA_ID" = "Batch"))
merge_subjects <- merge_subjects %>%
group_by(PSA_ID) %>%
mutate(Overall_N_Gender = sum(c(N.x, N.y), na.rm = T),
Overall_N_Female = sum(c(Female_N.x, Female_N.y), na.rm = T),
Overall_N_Male = sum(c(Male_N.x, Male_N.y), na.rm = T),
Overall_Age = mean(c(Age.x, Age.y), na.rm = T),
Overall_SD = mean(c(AgeSD.x, AgeSD.y), na.rm = T),
Overall_Missing_Age = sum(c(missing_age.x, missing_age.y), na.rm = T)) %>%
left_join(lab_info %>% select(PSA_ID, Language) %>% unique(), by = "PSA_ID")
flextable(merge_subjects %>%
select(1:5, 19:25)) %>%
set_header_labels(c("Lab ID Code", "SP Number Trials", "PP Number Trials",
"SP Participants", "PP Participants", "Demographic Sample Size",
"Female Sample Size", "Male Sample Size", "Average Age",
"SD Age", "Missing Age", "Language")) %>%
set_caption(caption = "Sample Size and Meta Data Information")
PSA_ID | SP_N_trials | PP_N_trials | SP_N | PP_N | Overall_N_Gender | Overall_N_Female | Overall_N_Male | Overall_Age | Overall_SD | Overall_Missing_Age | Language |
|---|---|---|---|---|---|---|---|---|---|---|---|
ARE_001 | 1,248 | 1,248 | 52 | 52 | 53 | 0 | 0 | 38.00000 | 52 | Arabic | |
ARE_002 | 1,296 | 1,296 | 54 | 54 | 54 | 42 | 12 | 26.51020 | 18.5877675 | 5 | Arabic |
AUS_002 | 2,376 | 2,376 | 99 | 99 | 103 | 46 | 37 | 20.14141 | 3.3228034 | 4 | English |
AUS_091 | 3,840 | 3,840 | 160 | 160 | 160 | 127 | 25 | 26.03185 | 11.5466836 | 3 | English |
AUT_002 | 2,400 | 2,400 | 100 | 100 | 114 | 0 | 1 | 20.94175 | 2.5622648 | 11 | German |
AUT_005 | 2,592 | 2,592 | 108 | 108 | 108 | 80 | 22 | 22.17925 | 4.2601077 | 2 | German |
BRA_003 | 1,200 | 1,200 | 50 | 50 | 50 | 36 | 13 | 30.80000 | 8.7341694 | 0 | Brazilian Portuguese |
CAN_020 | 2,352 | 2,376 | 98 | 99 | 104 | 54 | 40 | 20.25510 | 3.6589425 | 6 | English |
CHN_005 | 1,200 | 1,200 | 50 | 50 | 57 | 0 | 0 | 18.66038 | 3.9171389 | 4 | Simple Chinese |
CHN_019 | 840 | 816 | 35 | 34 | 39 | 0 | 1 | 25.17143 | 5.4367254 | 4 | Simple Chinese |
COL_001 | 1,680 | 1,656 | 70 | 69 | 70 | 0 | 0 | 21.35714 | 3.3623478 | 0 | Spanish |
DEU_020 | 624 | 624 | 26 | 26 | 26 | 18 | 3 | 23.88462 | 3.3861710 | 0 | German |
ECU_001 | 1,440 | 1,440 | 60 | 60 | 76 | 0 | 0 | 22.09722 | 4.2992293 | 4 | Spanish |
GBR_005 | 1,272 | 1,272 | 53 | 53 | 76 | 57 | 13 | 19.95890 | 3.8960035 | 3 | English |
GBR_006 | 1,200 | 1,200 | 50 | 50 | 51 | 37 | 13 | 20.14000 | 2.4578903 | 1 | English |
GBR_014 | 1,200 | 1,200 | 50 | 50 | 58 | 46 | 11 | 18.73684 | 1.6204752 | 1 | English |
GBR_043 | 720 | 720 | 30 | 30 | 32 | 15 | 11 | 25.70000 | 9.3960740 | 2 | English |
GRC_002 | 2,376 | 2,376 | 99 | 99 | 109 | 0 | 0 | 33.85981 | 11.3040722 | 2 | Greek |
HUN_001 | 3,610 | 3,816 | 151 | 159 | 168 | 3 | 1 | 21.50299 | 2.8153500 | 1 | Magyar |
IND_003 | 1,896 | 1,896 | 79 | 79 | 86 | 57 | 27 | 21.66265 | 3.4580310 | 3 | Hindi |
ISR_001 | 3,576 | 3,571 | 149 | 149 | 181 | 0 | 0 | 24.25309 | 9.2898875 | 19 | Hebrew |
MYS_003 | 1,200 | 1,248 | 50 | 52 | 52 | 38 | 11 | 22.56250 | 3.8971143 | 4 | English |
MYS_004 | 2,400 | 2,400 | 100 | 100 | 109 | 65 | 30 | 20.73148 | 2.0028320 | 1 | English |
NGA_001 | 1,248 | 1,248 | 52 | 52 | 52 | 24 | 22 | 23.94231 | 11.2901370 | 0 | English |
NOR_002 | 504 | 504 | 21 | 21 | 21 | 12 | 8 | 30.09524 | 8.5784892 | 0 | Norwegian |
NOR_003 | 1,320 | 1,320 | 55 | 55 | 53 | 1 | 1 | 23.55102 | 6.2452022 | 4 | Norwegian |
NOR_004 | 1,752 | 1,752 | 73 | 73 | 80 | 0 | 0 | 22.00000 | 4.3841509 | 2 | Norwegian |
NZL_005 | 7,680 | 7,680 | 320 | 320 | 320 | 244 | 56 | 23.21311 | 5.4287089 | 15 | English |
POL_001 | 1,368 | 1,368 | 57 | 57 | 146 | 0 | 0 | 23.24615 | 7.9553614 | 16 | Polish |
POR_001 | 1,488 | 1,464 | 62 | 61 | 55 | 26 | 23 | 30.74074 | 9.0890462 | 1 | Portuguese |
PSA_001 | 1,248 | 1,272 | 52 | 53 | 71 | 50 | 12 | 18.89231 | 0.9539896 | 6 | English |
PSA_002 | 1,536 | 1,536 | 64 | 64 | 102 | 79 | 11 | 19.82488 | 2.4172552 | 5 | English |
SRB_002 | 3,120 | 3,120 | 130 | 130 | 130 | 108 | 21 | 21.37500 | 4.5002187 | 2 | Serbian |
SVK_001 | 2,419 | 2,400 | 101 | 100 | 103 | 1 | 0 | 21.58824 | 2.5147208 | 1 | Slovak |
SVK_002 | 1,462 | 1,199 | 61 | 50 | 222 | 0 | 0 | 21.96172 | 2.1390304 | 13 | Slovak |
THA_001 | 1,200 | 1,152 | 50 | 48 | 50 | 29 | 9 | 21.54000 | 3.8130282 | 0 | Thai |
TUR_007 | 2,184 | 2,184 | 91 | 91 | 93 | 0 | 0 | 20.92308 | 2.9334499 | 2 | Turkish |
TUR_007E | 264 | 264 | 11 | 11 | 12 | 9 | 2 | 20.36364 | 1.9116865 | 1 | English |
TUR_023 | 1,896 | 1,896 | 79 | 79 | 80 | 36 | 14 | 21.57895 | 8.6379822 | 3 | Turkish |
TUR_025 | 2,376 | 2,352 | 99 | 98 | 101 | 0 | 0 | 21.63000 | 2.1911438 | 1 | Turkish |
TWN_001 | 1,440 | 1,440 | 60 | 60 | 70 | 45 | 14 | 20.72727 | 1.2095194 | 4 | Traditional Chinese |
TWN_002 | 2,160 | 2,160 | 90 | 90 | 116 | 24 | 32 | 21.04347 | 3.6608571 | 17 | Traditional Chinese |
TWN_002E | 288 | 288 | 12 | 12 | 12 | 6 | 5 | 21.16667 | 1.1934163 | 0 | English |
USA_011 | 1,512 | 1,512 | 63 | 63 | 63 | 30 | 23 | 22.34426 | 11.5453963 | 2 | English |
USA_020 | 7,980 | 8,064 | 333 | 336 | 403 | 258 | 76 | 19.63290 | 2.1214349 | 63 | English |
USA_030 | 648 | 648 | 27 | 27 | 31 | 20 | 3 | 36.00000 | 0.9607689 | 3 | English |
USA_032 | 1,209 | 1,224 | 51 | 51 | 51 | 30 | 21 | 19.29412 | 1.5138576 | 0 | English |
USA_033 | 3,000 | 3,024 | 125 | 126 | 129 | 90 | 25 | 20.05952 | 1.3554741 | 10 | English |
USA_065 | 1,200 | 1,200 | 50 | 50 | 61 | 35 | 15 | 18.85714 | 1.6340532 | 5 | English |
USA_173 | 792 | 744 | 33 | 31 | 3 | 0 | 3 | 19.66667 | 0.5773503 | 0 | English |
1 | 0 | 0 | 1 | ||||||||
0000 | 3 | 0 | 1 | 19.00000 | 2 | ||||||
040 | 1 | 0 | 0 | 1 | |||||||
1 | 2 | 0 | 0 | 100.00000 | 1 | ||||||
11 | 2 | 0 | 0 | 2 | |||||||
123 | 1 | 0 | 0 | 1 | |||||||
1234 | 1 | 0 | 0 | 1 | |||||||
149 | 1 | 0 | 0 | 20.00000 | 0 | ||||||
173 | 1 | 0 | 1 | 22.00000 | 0 | ||||||
19 | 1 | 0 | 0 | 1 | |||||||
23232 | 1 | 0 | 0 | 1 | |||||||
26844 | 1 | 0 | 0 | 22.00000 | 0 | ||||||
3726 | 6 | 0 | 0 | 33.00000 | 5 | ||||||
54 | 1 | 0 | 0 | 23.00000 | 0 | ||||||
75AB | 1 | 0 | 0 | 1 | |||||||
87 | 1 | 0 | 0 | 29.00000 | 0 | ||||||
9317 | 2 | 0 | 0 | 2 | |||||||
94 | 1 | 0 | 0 | 23.00000 | 0 | ||||||
9951 | 1 | 0 | 1 | 22.00000 | 0 | ||||||
BRA_004 | 1 | 0 | 0 | 1 | Portuguese | ||||||
MAC_001 | 1 | 0 | 0 | 1 | |||||||
MYS_033 | 1 | 1 | 0 | 19.00000 | 0 | ||||||
POL_003 | 1 | 0 | 0 | 1 | |||||||
SKV_002 | 1 | 0 | 0 | 22.00000 | 0 | ||||||
USA_002 | 1 | 0 | 0 | 1 | |||||||
USA_003 | 3 | 2 | 1 | 19.33333 | 0.5773503 | 0 | |||||
jnkjn | 1 | 0 | 0 | 1 |
codebook_data <- bind_rows(SP_V, PP)
var_label(codebook_data) <- list(
PSA_ID = "Lab identification code",
subject_nr = "Original, lab assigned subject number",
LAB_SEED = "Original, lab assigned seed number for randomization",
datetime = "Date and time of the study",
logfile = "Original location of the saved log",
task_order = "",
List = "List file for the presentation of the stimuli",
Match = "Match or Mismatch of the sentence and picture for sentence picture trials. F indicates practical trials.",
Orientation = "Direction of the stimuli picture presented on the screen",
PList = "List file for the practice stimuli presentation",
Probe = "Sentence seen in the sentence picture matching task",
Target = "Object seen in the sentence picture matching task",
response_time = "Response time to determine if objects or sentence/picture matched",
correct = "Indicates if the participant answered the trial correctly",
opensesame_codename = "Name of the version of open sesame",
opensesame_version = "Version number of the open sesame used",
Language = "Language the participant took the study in",
Source = "Online (osweb) versus in person (all others) data source",
Subject = "A unique participant identifier, as duplicates and other repeating trials were fixed",
PPList = "The stimulus presentation list for picture picture matching task",
Orientation1 = "The orientation of the first picture in the picture picture matching task",
Orientation2 = "The orientation of the second picture in the picture picture matching task",
Identical = "If the two orientations of the pictures matched in the picture picture matching task",
Picture1 = "Name of the first picture in the picture picture matching task",
Picture2 = "Name of the secon picture in the picture picture matching task"
)
metadata(codebook_data)$name <- "Object Orientation across languages"
metadata(codebook_data)$description <- "This dataset includes the raw trial data from the PSA002: Object Orientation Across Languages Study.
Mental simulation theories of language comprehension propose that people automatically create mental representations of objects mentioned in sentences. Representation is often measured with the sentence-picture verification task, in which participants first read a sentence implying the shape/size/color/object orientation and, on the following screen, a picture of an object. Participants then verify if the pictured object either matched or mismatched the implied visual information mentioned in the sentence. Previous studies indicated the match advantages of shapes, but findings concerning object orientation were mixed across languages. This registered report describes our investigation of the match advantage of object orientation across 18 languages, which was undertaken by multiple laboratories and organized by the Psychological Science Accelerator. The preregistered analysis revealed that there is no compelling evidence for a global match advantage, although some evidence of a match advantage in one language was found. Additionally, the match advantage was not predicted by mental rotation scores which does not support current embodied cognition theories."
metadata(codebook_data)$identifier <- "https://osf.io/e428p/"
metadata(codebook_data)$creator <- "Erin M. Buchanan"
metadata(codebook_data)$citation <- "Chen et al. (2023). Investigating Object Orientation Effects Across 18 Languages. Registered Report."
metadata(codebook_data)$url <- "https://osf.io/e428p/"
metadata(codebook_data)$datePublished <- "2023-02-10"
metadata(codebook_data)$temporalCoverage <- "2019-2021"
metadata(codebook_data)$spatialCoverage <- "Online and in Person"
codebook(codebook_data)
Dataset name: Object Orientation across languages
This dataset includes the raw trial data from the PSA002: Object Orientation Across Languages Study.
Mental simulation theories of language comprehension propose that people automatically create mental representations of objects mentioned in sentences. Representation is often measured with the sentence-picture verification task, in which participants first read a sentence implying the shape/size/color/object orientation and, on the following screen, a picture of an object. Participants then verify if the pictured object either matched or mismatched the implied visual information mentioned in the sentence. Previous studies indicated the match advantages of shapes, but findings concerning object orientation were mixed across languages. This registered report describes our investigation of the match advantage of object orientation across 18 languages, which was undertaken by multiple laboratories and organized by the Psychological Science Accelerator. The preregistered analysis revealed that there is no compelling evidence for a global match advantage, although some evidence of a match advantage in one language was found. Additionally, the match advantage was not predicted by mental rotation scores which does not support current embodied cognition theories.
Temporal Coverage: 2019-2021
Spatial Coverage: Online and in Person
Citation: Chen et al. (2023). Investigating Object Orientation Effects Across 18 Languages. Registered Report.
Identifier: https://osf.io/e428p/
Date published: 2023-02-10
Creator:
| name | value |
|---|---|
| 1 | Erin M. Buchanan |
|
#Variables
Lab identification code
Distribution of values for PSA_ID
0 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| PSA_ID | Lab identification code | character | 0 | 1 | 50 | 0 | 7 | 8 | 0 |
Original, lab assigned subject number
Distribution of values for subject_nr
0 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| subject_nr | Original, lab assigned subject number | character | 0 | 1 | 2844 | 0 | 1 | 22 | 0 |
Original, lab assigned seed number for randomization
Distribution of values for LAB_SEED
67464 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| LAB_SEED | Original, lab assigned seed number for randomization | character | 67464 | 0.6479098 | 52 | 0 | 2 | 5 | 0 |
Date and time of the study
Distribution of values for datetime
0 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| datetime | Date and time of the study | character | 0 | 1 | 6410 | 0 | 13 | 71 | 0 |
Original location of the saved log
Distribution of values for logfile
67416 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| logfile | Original location of the saved log | character | 67416 | 0.6481603 | 5142 | 0 | 8 | 105 | 0 |
Distribution of values for task_order
129498 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| task_order | character | 129498 | 0.3241584 | 3 | 0 | 2 | 13 | 0 |
List file for the presentation of the stimuli
Distribution of values for List
95778 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| List | List file for the presentation of the stimuli | character | 95778 | 0.5001409 | 68 | 0 | 1 | 15 | 0 |
Match or Mismatch of the sentence and picture for sentence picture trials. F indicates practical trials.
Distribution of values for Match
95778 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| Match | Match or Mismatch of the sentence and picture for sentence picture trials. F indicates practical trials. | character | 95778 | 0.5001409 | 2 | 0 | 1 | 1 | 0 |
Direction of the stimuli picture presented on the screen
Distribution of values for Orientation
95778 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| Orientation | Direction of the stimuli picture presented on the screen | character | 95778 | 0.5001409 | 2 | 0 | 1 | 1 | 0 |
List file for the practice stimuli presentation
Distribution of values for PList
101514 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| PList | List file for the practice stimuli presentation | character | 101514 | 0.4702051 | 34 | 0 | 1 | 14 | 0 |
Sentence seen in the sentence picture matching task
Distribution of values for Probe
95778 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| Probe | Sentence seen in the sentence picture matching task | character | 95778 | 0.5001409 | 1096 | 0 | 11 | 132 | 0 |
Object seen in the sentence picture matching task
Distribution of values for Target
95778 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| Target | Object seen in the sentence picture matching task | character | 95778 | 0.5001409 | 48 | 0 | 8 | 22 | 0 |
Response time to determine if objects or sentence/picture matched
Distribution of values for response_time
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| response_time | Response time to determine if objects or sentence/picture matched | numeric | 0 | 1 | 0 | 609 | 472186 | 815.4908 | 2870.269 | ▇▁▁▁▁ |
Indicates if the participant answered the trial correctly
Distribution of values for correct
1 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| correct | Indicates if the participant answered the trial correctly | numeric | 1 | 0.9999948 | 0 | 1 | 1 | 0.9289334 | 0.2569367 | ▁▁▁▁▇ |
Name of the version of open sesame
Distribution of values for opensesame_codename
0 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| opensesame_codename | Name of the version of open sesame | character | 0 | 1 | 3 | 0 | 5 | 17 | 0 |
Version number of the open sesame used
Distribution of values for opensesame_version
0 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| opensesame_version | Version number of the open sesame used | character | 0 | 1 | 7 | 0 | 5 | 10 | 0 |
Language the participant took the study in
Distribution of values for Language
0 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| Language | Language the participant took the study in | character | 0 | 1 | 18 | 0 | 4 | 20 | 0 |
Online (osweb) versus in person (all others) data source
Distribution of values for Source
0 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| Source | Online (osweb) versus in person (all others) data source | character | 0 | 1 | 2 | 0 | 4 | 5 | 0 |
A unique participant identifier, as duplicates and other repeating trials were fixed
Distribution of values for Subject
0 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| Subject | A unique participant identifier, as duplicates and other repeating trials were fixed | character | 0 | 1 | 4248 | 0 | 25 | 87 | 0 |
The stimulus presentation list for picture picture matching task
Distribution of values for PPList
95832 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| PPList | The stimulus presentation list for picture picture matching task | character | 95832 | 0.4998591 | 8 | 0 | 1 | 12 | 0 |
The orientation of the first picture in the picture picture matching task
Distribution of values for Orientation1
95832 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| Orientation1 | The orientation of the first picture in the picture picture matching task | character | 95832 | 0.4998591 | 2 | 0 | 1 | 1 | 0 |
The orientation of the second picture in the picture picture matching task
Distribution of values for Orientation2
95832 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| Orientation2 | The orientation of the second picture in the picture picture matching task | character | 95832 | 0.4998591 | 2 | 0 | 1 | 1 | 0 |
If the two orientations of the pictures matched in the picture picture matching task
Distribution of values for Identical
95832 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| Identical | If the two orientations of the pictures matched in the picture picture matching task | character | 95832 | 0.4998591 | 2 | 0 | 1 | 1 | 0 |
Name of the first picture in the picture picture matching task
Distribution of values for Picture1
95832 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| Picture1 | Name of the first picture in the picture picture matching task | character | 95832 | 0.4998591 | 48 | 0 | 8 | 22 | 0 |
Name of the secon picture in the picture picture matching task
Distribution of values for Picture2
95832 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| Picture2 | Name of the secon picture in the picture picture matching task | character | 95832 | 0.4998591 | 48 | 0 | 8 | 22 | 0 |
The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.
{
"name": "Object Orientation across languages",
"description": "This dataset includes the raw trial data from the PSA002: Object Orientation Across Languages Study. \n\nMental simulation theories of language comprehension propose that people automatically create mental representations of objects mentioned in sentences. Representation is often measured with the sentence-picture verification task, in which participants first read a sentence implying the shape/size/color/object orientation and, on the following screen, a picture of an object. Participants then verify if the pictured object either matched or mismatched the implied visual information mentioned in the sentence. Previous studies indicated the match advantages of shapes, but findings concerning object orientation were mixed across languages. This registered report describes our investigation of the match advantage of object orientation across 18 languages, which was undertaken by multiple laboratories\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name |label | n_missing|\n|:-------------------|:--------------------------------------------------------------------------------------------------------|---------:|\n|PSA_ID |Lab identification code | 0|\n|subject_nr |Original, lab assigned subject number | 0|\n|LAB_SEED |Original, lab assigned seed number for randomization | 67464|\n|datetime |Date and time of the study | 0|\n|logfile |Original location of the saved log | 67416|\n|task_order | | 129498|\n|List |List file for the presentation of the stimuli | 95778|\n|Match |Match or Mismatch of the sentence and picture for sentence picture trials. F indicates practical trials. | 95778|\n|Orientation |Direction of the stimuli picture presented on the screen | 95778|\n|PList |List file for the practice stimuli presentation | 101514|\n|Probe |Sentence seen in the sentence picture matching task | 95778|\n|Target |Object seen in the sentence picture matching task | 95778|\n|response_time |Response time to determine if objects or sentence/picture matched | 0|\n|correct |Indicates if the participant answered the trial correctly | 1|\n|opensesame_codename |Name of the version of open sesame | 0|\n|opensesame_version |Version number of the open sesame used | 0|\n|Language |Language the participant took the study in | 0|\n|Source |Online (osweb) versus in person (all others) data source | 0|\n|Subject |A unique participant identifier, as duplicates and other repeating trials were fixed | 0|\n|PPList |The stimulus presentation list for picture picture matching task | 95832|\n|Orientation1 |The orientation of the first picture in the picture picture matching task | 95832|\n|Orientation2 |The orientation of the second picture in the picture picture matching task | 95832|\n|Identical |If the two orientations of the pictures matched in the picture picture matching task | 95832|\n|Picture1 |Name of the first picture in the picture picture matching task | 95832|\n|Picture2 |Name of the secon picture in the picture picture matching task | 95832|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
"identifier": "https://osf.io/e428p/",
"creator": "Erin M. Buchanan",
"citation": "Chen et al. (2023). Investigating Object Orientation Effects Across 18 Languages. Registered Report.",
"url": "https://osf.io/e428p/",
"datePublished": "2023-02-10",
"temporalCoverage": "2019-2021",
"spatialCoverage": "Online and in Person",
"keywords": ["PSA_ID", "subject_nr", "LAB_SEED", "datetime", "logfile", "task_order", "List", "Match", "Orientation", "PList", "Probe", "Target", "response_time", "correct", "opensesame_codename", "opensesame_version", "Language", "Source", "Subject", "PPList", "Orientation1", "Orientation2", "Identical", "Picture1", "Picture2"],
"@context": "http://schema.org/",
"@type": "Dataset",
"variableMeasured": [
{
"name": "PSA_ID",
"description": "Lab identification code",
"@type": "propertyValue"
},
{
"name": "subject_nr",
"description": "Original, lab assigned subject number",
"@type": "propertyValue"
},
{
"name": "LAB_SEED",
"description": "Original, lab assigned seed number for randomization",
"@type": "propertyValue"
},
{
"name": "datetime",
"description": "Date and time of the study",
"@type": "propertyValue"
},
{
"name": "logfile",
"description": "Original location of the saved log",
"@type": "propertyValue"
},
{
"name": "task_order",
"description": "",
"@type": "propertyValue"
},
{
"name": "List",
"description": "List file for the presentation of the stimuli",
"@type": "propertyValue"
},
{
"name": "Match",
"description": "Match or Mismatch of the sentence and picture for sentence picture trials. F indicates practical trials.",
"@type": "propertyValue"
},
{
"name": "Orientation",
"description": "Direction of the stimuli picture presented on the screen",
"@type": "propertyValue"
},
{
"name": "PList",
"description": "List file for the practice stimuli presentation",
"@type": "propertyValue"
},
{
"name": "Probe",
"description": "Sentence seen in the sentence picture matching task",
"@type": "propertyValue"
},
{
"name": "Target",
"description": "Object seen in the sentence picture matching task",
"@type": "propertyValue"
},
{
"name": "response_time",
"description": "Response time to determine if objects or sentence/picture matched",
"@type": "propertyValue"
},
{
"name": "correct",
"description": "Indicates if the participant answered the trial correctly",
"@type": "propertyValue"
},
{
"name": "opensesame_codename",
"description": "Name of the version of open sesame",
"@type": "propertyValue"
},
{
"name": "opensesame_version",
"description": "Version number of the open sesame used",
"@type": "propertyValue"
},
{
"name": "Language",
"description": "Language the participant took the study in",
"@type": "propertyValue"
},
{
"name": "Source",
"description": "Online (osweb) versus in person (all others) data source",
"@type": "propertyValue"
},
{
"name": "Subject",
"description": "A unique participant identifier, as duplicates and other repeating trials were fixed",
"@type": "propertyValue"
},
{
"name": "PPList",
"description": "The stimulus presentation list for picture picture matching task",
"@type": "propertyValue"
},
{
"name": "Orientation1",
"description": "The orientation of the first picture in the picture picture matching task",
"@type": "propertyValue"
},
{
"name": "Orientation2",
"description": "The orientation of the second picture in the picture picture matching task",
"@type": "propertyValue"
},
{
"name": "Identical",
"description": "If the two orientations of the pictures matched in the picture picture matching task",
"@type": "propertyValue"
},
{
"name": "Picture1",
"description": "Name of the first picture in the picture picture matching task",
"@type": "propertyValue"
},
{
"name": "Picture2",
"description": "Name of the secon picture in the picture picture matching task",
"@type": "propertyValue"
}
]
}`